关键词: 贝叶斯网络, 结构学习, Jaya算法, 马尔科夫链

Abstract: Bayesian network structure learning algorithms based on scoring search need parameter tuning, which increases computational complexity. Improper parameters make the algorithm fall into local optimum. Without tuning, the Jaya algorithm is applied to Bayesian network structure learning. In the Jaya algorithm framework, combined with cross-variation of genetic algorithm, the individual update strategy is redesigned. Consequently, Jaya algorithm can be applied to the discrete structure learning optimization problem. The convergence of the proposed algorithm is discussed with the Markov chain theory. Experimental results show that the algorithm can be effectively applied to Bayesian network structure learning.

Key words: Bayesian network, structural learning, Jaya algorithm, Markov chain